Autonomy

1
Autonomy

• Model-based Embedded and
• Robotic Systems Group (MERS)
• Graduate Open House

2
The Need for Autonomy

• Space systems must handle
• Faults and anomalies
• Cooperative exploration
• Long-term science operations
• Increasingly ambitious goals

Europa Probe
MER Memory Leak
Apollo 13 quintuple fault
Mars Outpost
Earth Imager
Mars Polar Lander failed due to a faulty sensor.
3
Future Autonomous Vehicles
Space Technology 3
Europa Hydrobot
In-Situ Propellant Plant
Orbital Space Plane
Mars Life Support Facility
2009 Mars Science Lab
4
Mission Collaboration
MER Shadow Mode
Orbital Express
MIT SPHERES
Earth Observing-1
Mars Science Laboratory 2009
Images courtesy of NASA JPL
5
Robust Systems Should beFully State Aware

• Embedded programs interact with the systems
  sensors/actuators
• Read sensors
• Set actuators

• Model-based programs interact with the systems
  hidden state
• Read state
• Set state

Embedded Program
Control
Observations
Observations
Control

• Programmer must map between hidden state and
  sensors/actuators.

• M-B Executive maps between hidden state and
  sensors/actuators.

6
Example Orbital Insertion Scenario
Engine Model
(thrust zero) AND (power_in zero)
Off
0.01
off- cmd
Failed
standby- cmd
(thrust zero) AND (power_in nominal)
0.01
EngineA
EngineB
EngineA
EngineB
Standby
fire- cmd
standby- cmd
0.01
(thrust full) AND (power_in nominal)
Firing
Science Camera
Science Camera
Systems engineers think in terms of state
trajectories
Camera Model
Off

• must fire one of the two engines
• set both engines to standby
• prior to firing engine, camera must be turned off
  to avoid plume contamination
• in case of primary engine failure, fire backup
  engine instead

(power_in zero) AND (shutter closed)
turnoff- cmd
turnon- cmd
(power_in nominal) AND (shutter open)
On
7
Model-based Program

• Control program specifies state trajectories
• Concurrency
• Preemption
• Queries hidden state
• Asserts (assigns) hidden state

OrbitInsert() (do-watching ((EngineA
Firing) OR (EngineB
Firing)) (parallel (EngineA
Standby) (EngineB Standby)
(Camera Off) (do-watching (EngineA
Failed) (when-donext (
(EngineA Standby) AND
(Camera Off) )
(EngineA Firing)))
(when-donext ( (EngineA Failed) AND
(EngineB Standby)
AND
(Camera Off) ) (EngineB
Firing))))

• Plant Model describes behavior of each component
• Nominal and off-nominal
• Qualitative constraints
• Likelihoods and costs

Models are reusable and easy to articulate at the
conceptual stage
8
Titan Model-based Executive
Model-based Executive
RMPL
Control program
Sequencer / Planner Generates target goal states
conditioned onstate estimates

• Executes concurrently
• Preempts
• Queries (hidden) states
• Asserts (hidden) state

State goals
State estimates
System model
Mode Estimation tracks likelyplant state
Mode Reconfiguration tracks least costgoal states
Commands
Observations
Plant
9
Example The model-based program sets the state
to thrusting, and the M-B executive . . .
Fuel tank
Oxidizer tank
Plans actions to open six valves
Deduces that thrust is off, andthe engine is
healthy
Deduces that a valve failed - stuck closed
Determines that valves on the backup enginewill
achieve thrust, andplans needed actions.
10
Mode Estimation

• Purpose Ideal mode estimation would maintain a
  complete belief state
• Belief State Probability distribution across
  all combinations of possible states in the system
• Challenge Combination of states is exponential
  in the number of modes
• Solution Simplifying assumption to track only k
  trajectories (shown below) reduces the space
  requirement to linear!

Concurrent Constraint Automata
Switch and OR-Gate System

• Tracking a history of the system state over time

t0
t1
t2

• cmd sw1-turnOn
• Obs LED-off

• cmd sw2-turnOn
• Obs LED-off

sw1bkn, sw2off, ornom
sw1off, sw2off, orbkn
likelihood
sw1off, sw2off, ornom
sw1off, sw2bkn, ornom
sw1bkn, sw2bkn, ornom
sw1off, sw2off, orbkn
sw1bkn, sw2bkn, orbkn
sw1bkn, sw2bkn, ornom

11
Compiled Mode Estimation
Compiled Mode Estimation
Online
Offline
Projected Prime Implicate Generation
Best-first
Dissents
Partial
Conflicts
Kernel
Most Likely
System
Diagnosis
Diagnosis
Diagnosis
Model
Trigger
Enabled Modes
Generator
Compiled Transitions
Discrete
Continuous
Monitors
Observations
Observations

• Off-line Operations (Removes the need for
  NP-complete online satisfiability)

• Dissent A mapping from observations to conflicts

(LEDoff) ? sw1on ? sw2on ? ornom ....
Model Compilation

• On-line Operations (Reduced to an optimal search
  instead of OCSP)

• Most Likely Diagnosis
• Or-gate Nominal
• Switch1 On
• Switch2 Broken

Partial Diagnosis Trigger
(LEDoff)
12
Hybrid Mode Estimation

• failures can manifest themselves through coupling
  between a systems continuous dynamics and its
  evolution through different behavior modes
• must track over continuous state changes and
  discrete mode changes
• symptoms initially on the same scale as
  sensor/actuator noise
• need to extract mode estimates from subtle
  symptoms

old estimate Xk-1mi,xk-1
new estimate Xkmj,xk
Xk-1mj,xk-1
yc(k)

KalmanFilter Bank

Xk
Mode Estimation
xci(k)
Hybrid Mode Estimation tracks a set of
trajectories
Pi(k)
uc(k-1)

• Methods
• K-best filtering,
• Rao-Blackwellised particle filtering

Ck
13
Application Gesture Recognition

• Robonaut
• EVA astronauts assistant
• Humanoid design requires no specialized robotic
  tools
• Controlled by teleoperator, but autonomous modes
  under development

• Gesture recognition
• Stereo vision system
• Tracks head and hand motion of human associate
• Hybrid model supports Robonauts recognition of
  human gestures
• Gestures of interest include pointing to a tool,
  holding hand up to indicate stop, come closer
  gestures, etc.
• Continuous dynamics model of human arm includes
  inertial and damping terms
• HMM model takes output of stereo vision system as
  observation
• Transitions between motion control point states

14
Mode Reconfiguration

• INPUT
• Configuration Goal
• Thrust on
• Current State
• Tank full
• Pressure nominal
• Driver off
• Valve closed
• Thruster off

• OUTPUT
• Command
• Turn driver on

15
Goal Interpreter

• INPUT
• Current State
• Tank full
• Pressure nominal
• Driver off
• Valve closed
• Thruster off
• Configuration Goal
• Thrust on

• OUTPUT
• Goal State
• Tank full
• Pressure nominal
• Driver off
• Valve on
• Thruster on

Generate optimal goal state that achieves the
Configuration Goal!

• Goal Interpreter

• Compiled Goal Interpreter

Minimize online deduction by generating all
partial goal interpretation offline! Online
Goal State
Goal Configuration
Partial Goal Interpretation
Best-first Kernel Goal State Generator
16
Reactive Planner

• INPUT
• Current State
• Tank full
• Pressure nominal
• Driver off
• Valve closed
• Thruster off
• Goal State
• Tank full
• Pressure nominal
• Driver off
• Valve on
• Thruster on

• OUTPUT
• Command
• Turn driver on

• Planner guarantees to
• Only generate non-destructive actions
• Never propose actions that lead to dead-end plans
• Ensure progress toward the goal
• Operate at reactive time scale

Valve
Driver

• Reconfiguration
• Order
• Tank full
• Pressure nominal
• Valve on
• Thruster on
• Driver off

Goal
Goal
Current
Current
Open
Closed
On
Off
idle
driver on cmd close
idle
cmd off
Open
On
driver on cmd open
idle
cmd on
idle
Off
Closed
fail
fail
cmd reset
cmd off
Stuck
Resettable
17
Verification of RMPL Programs

• Motivation
• Want robust autonomous systems.
• Extend traditional scenario-based testing to
  verification and validation (VV).

Approach

• Goals
• Verify RMPL model-based programs (control program
  plant model) against goal specification.
• e.g., ((EngineA Firing) OR (EngineB Firing))
  for OrbitInsert()
• Extract probabilistic information about programs
  possible executions.

18
Programming Cooperative TeamsTo Perform Global
Science
TWO
ONE
HOME
Enroute
COLLECTION POINT
RENDEZVOUS
Landing Site ABC
Diverge
Landing Site XYZ
SCIENCE AREA 1
SCIENCE AREA 1
SCIENCE AREA 3

• Mission controller specifies abstract set of
  goals for a robot team
• System must handle
• Low-level planning and execution
• Dynamic environment
• Inter-vehicle communication

19
Heterogeneous Robots

• Orbiter
• Earth Com link,
• Large scale feature detection
• Science observation
• Tethered Blimp
• Reconnaissance Rover tracking, feature
  detection, local map generator
• Sensor network deployment
• Rover Com link
• Smart Mobile Lander
• Slow mobile base station
• Orbiter Com link
• Large science package
• Scout Rovers
• Fast agile rovers
• Sensor package for identifying science objectives
• Terrain mapping functionality
• Sensor Network
• Highly constrained sensing/effecting
  communication array
• Science sensing

High Tier
Mid Tier
Low Tier
20
Programming Teams in RMPL

• RMPL Programs
• Describe concurrent sensing, actuation and
  movements activities.
• Choose specifies redundant strategies and
  contingencies.
• A,B Specifies timing constraints.

Corridor 2
Rendezvous
Rescue Area
Corridor 1
Enroute

• (Group-Enroute() l,u (
• (sequence
• choose (
• (do-watching (PATH1OK)
• ((Group-Traverse-Path(PATH1_1,PATH1_2,PAT
  H1_3,RE_POS))l90,u90)
• )
• (do-watching (PATH2OK)
• ((Group-Traverse-Path(PATH2_1,PATH2_2,PAT
  H2_3,RE_POS))l90,u90)
• ))
• (parallel
• ((Group-Transmit(OPS,ARRIVED))0,2)
• (do-watching(PROCEEDSIGNALLED)
• ((Group-Wait(HOLD1,HOLD2))0,u10))
• )))

21
Planning and Execution
RMPLCompiler
Represents all possible executions
RMPLProgram
Temporal Planner
Temporal Plan Network
Mission Specification
(choose (parallel (power low) 10,15)
(actB() 20,30) ) (if-thennext-elsenext
(state true) (macroA() 5,5)
(macroB() 6,8) ))
SolutionPlan
Plan Runner/ Dispatcher
Hardware
HardwareCommands
Planning and Execution
22
Distributed Planning and Execution

• Why distributed?
• to avoid mission being dependent on a single
  planning and execution robot
• distributed execution is essential when
  communication is limited
• resource constrained robots or sensor networks
  cannot run a big planning system

mission

• Challenges
• Limited communication
• Plan failures
• Robot failures
• Temporarily flexible plans

23
Integrated Activity and Path Planning for Agile
Teams
Path Planning Method 1 Explore state space using
Rapidly-exploring Random Trees (RRTs)

• Integrated Activity Planning and Path Planning
• Search a temporal plan network in best-first
  order
• Dynamically compute collision-free paths for
  those plan activities that require moving between
  locations and the estimated cost of flying along
  this path
• Continuously interleave activity and path
  planning to pursue the most promising plan.

Location A start state
Location B goal state
RRT
Maneuver Automaton Describes a set of agile
maneuvers with respect to the vehicles dynamics
Path Planning Method 2 Clausal Linear Programming
24
Path Planning through Clausal LP
A simple example

• Mathematically solving the problem of vehicle
  control normally involves straightforward Linear
  Programming
• But the addition of obstacle avoidance introduces
  an Integer Programming element
• This makes the problem difficult to solve
  online fast enough for actual vehicles in
  motion
• To resolve this we transform obstacle and
  collision avoidance into a Constraint
  Satisfaction Problem For each obstacle, the
  domain is split into four regions (above, below,
  left, right), one of which is selected
• Integrating the selection of domains with the
  standard vehicle control leads to a an algorithm
  that can be used as a Hybrid CSP/LP Solver

O2
O2 B
O1
O3 A
O3
O1 L

• si1 Asi Bui State Evolution Equation
• sij wij, etc. State Space Constraints
• xi xmin ? xi xmax ? yi ymin ? yi ymax
  Obstacle Avoidance (for all time i)
• Similar equation for Collision Avoidance (for all
  pairs of vehicles)

25
Mars Shadow Mode Project

• Simulate Mission Objectives of Mars 03
• Use NASAs MERBoard to visualize the environment
  and control the rovers.
• Demonstrate the ability to achieve mission goals
  autonomously

Analyze this rock!

• Rover Sensors
• Stereo camera head
• Laser range scanner
• Sonar array
• DGPS
• Digital compass
• Inclinometer

Remote operations center
Mars yard